We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods.
(1) Statistics is an applied field with a wide range of practical applications.
(2) You don’t have to be a math guru to learn from real, interesting data.
(3) Data are messy, and statistical tools are imperfect. But, when you understand the strengths and weaknesses of these tools, you can use them to learn about the real world.

Textbook overview

The chapters of this book are as follows:
1. Introduction to data. Data structures, variables, summaries, graphics, and basic data collection techniques.
2. Probability (special topic). The basic principles of probability. An understanding of this chapter is not required for the main content in Chapters 3-8.
3. Distributions of random variables. Introduction to the normal model and other key distributions.
4. Foundations for inference. General ideas for statistical inference in the context of estimating the population mean.
5. Inference for numerical data. Inference for one or two sample means using the t-distribution, and also comparisons of many means using ANOVA.
6. Inference for categorical data. Inference for proportions using the normal and chisquare distributions, as well as simulation and randomization techniques.
7. Introduction to linear regression. An introduction to regression with two variables. Most of this chapter could be covered after Chapter 1.
8. Multiple and logistic regression. A light introduction to multiple regression and logistic regression for an accelerated course.

OpenIntro Statistics was written to allow flexibility in choosing and ordering course topics. The material is divided into two pieces: main text and special topics. The main text has been structured to bring statistical inference and modeling closer to the front of a course. Special topics, labeled in the table of contents and in section titles, may be added to a course as they arise naturally in the curriculum.